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In mathematical theory, the discrete cosine transform (DCT) is a lossless orthogonal transformation method which means it outputs exactly the same values of the input after the inverse transformation. However, this is impossible in today’s technology due to the limited capacity of processors in which the maximum value that a number can take is 2 64- 1 (20-digit number) in a 64-bit register. Since the DCT employs the floating values higher than this precision, there occurs a round-off error which causes a particular loss of information after the inverse transformation. For this reason, the dynamic range of the DCT coefficients should be reduced so that fewer precision digits are employed in the DCT calculations, thereby the round-off error and loss of information are minimized. In this study, conventional DCT equations are improved both in forward and inverse transformation for the sake of high-performance JPEG image compression. The proposed method reduces the dynamic range of the DCT coefficients and provides a low dynamic range DCT (LDR-DCT) by weighting the DCT coefficients with respect to the frequency level. The effectiveness of the proposed LDR-DCT method is experimented mainly by observing the inter-correlation between the compression ratio and the peak signal-to-noise ratio (PSNR) values which is defined as the compression performance (CP). An extensive experimental benchmarking study is done using the publicly available KODAK image dataset in both grayscale and RGB color spaces, separately. According to the experimental results, the average compression performance (CP) is increased up to about 26% in grayscale images and about 17% in RGB images when the quantization factors (21–121) are employed in the quantization process. Additionally, it is observed that there is an average increment in the compression performance (CP) up to about 8% in grayscale images and about 7% in RGB images when the standard IrfanView quantization tables (quality level of 40 to the quality level of 90) are applied. On the other hand, in the absence of quantization when either the quantization factor of 1 or the standard IrfanView quantization table with the quality level of 100 is applied, it is also observed that there is an average increment in the PSNR value up to about 15% in grayscale images and about 33% in RGB images with respect to the average PSNR values of 24 images in the KODAK image dataset. Therefore, though the proposed LDR-DCT method without quantization does not change the compression ratio, it improves the quality of the output obtained after the inverse transform dramatically. In other words, the conventional DCT method should be replaced by the proposed LDR-DCT method in certain areas where compression is not required. Besides, the study claims that the proposed LDR-DCT method can provide at least the same JPEG image quality as the conventional DCT method with much higher compression ratios if the quantization tables are redesigned accordingly. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Objective. In this study, technical performance of the new multiplex ALEX test was compared with results from ImmunoCAP single tests (tIgE, sIgE) and the multiplex platform ImmunoCAP ISAC sIgE 112. Materials and methods. Eleven whole allergen extracts and corresponding allergen components from different allergen groups were used for the analysis of 64-66 patients’ sera by all three platforms. Results. For the whole allergens, 55% false negative results were obtained with the ALEX test comparing to the ImmunoCAP sIgE tests while for allergen components the ALEX test gives 33% false negative results when compared to ImmunoCAP sIgE test results. Additionally, the ALEX test is characterized by a low dynamic range - the platform demonstrated no results above 36 kUA/L for samples giving >100 kUA/L using ImmunoCAP Specific IgE tests in the analysis of sIgE response to the whole allergens. For the allergen components, ALEX showed no results above 38 kUA/L for samples of up to 150 kUA/L according to ImmunoCAP Specific IgE test results. Comparing to ImmunoCAP single plex tests, ALEX show low dynamic range and poor agreement in quantitative results for tIgE and sIgE both for whole allergens and allergen components, while in the comparison with ImmunoCAP ISAC sIgE 112 platform, the agreement is better, but the sensitivity and dynamic range are still low._ Conclusions. The ALEX test has some serious limitations in its performance comparing to both types of ImmunoCAP platforms.

A High Dynamic Range (HDR) image produced from a sequence of low dynamic range (LDR) images can contain motion artefacts (ghosting) if the scene contains moving objects. Conventional de-ghosting methods first detect moving objects in the scene, and then either remove those moving objects totally or reconstruct them. However, these methods are computationally expensive. This paper proposes a de-ghosting method that does not require explicit detection of moving regions. First, the ratio between camera exposure times of a target image and a reference image, which is called the intensity scaling factor in this paper, is computed. Since the information about camera exposure time is not available always, we propose a novel method to estimate the intensity scaling factor from non-saturated and non-moving pixels. Then, the estimated scaling factor is used as a cue to label every pixel in the target image as either static or moving pixel. Finally, the values of moving pixels are corrected with their expected values which can be estimated from the intensity scaling factor. Experimental results show that the proposed method generates more accurate ghost-free HDR images than the existing state of the art methods.

It is an ill-posed problem to restore a high dynamic range (HDR) image from one low dynamic range (LDR) image. Instead of using conventional reverse tone mapping to restore the HDR image, two differently exposed LDR images are generated from the underlying LDR image by fusing model-driven and data-driven approaches. One is brighter than the input image and the other is darker than the input image. The three images are fused together via a multi-scale exposure fusion algorithm to produce a desired HDR image which can be displayed directly by existing LDR digital devices. The proposed algorithm can be embedded in smart phones or digital cameras to produce a pseudo-HDR image.

Infrared image enhancement is a challenging task due to several factors such as low dynamic range, noise and non-uniformity effect. The non-uniformity is a time-dependent noise that appears owing to the lack of sensor equalization. This paper presents two proposed approaches for infrared video enhancement. The first proposed approach depends on histogram matching. The second one depends on contrast limited adaptive histogram equalization (CLAHE) and fuzzy logic. The performance metrics of average gradient, entropy, contrast improvement factor and Sobel edge magnitude are used for evaluating the obtained results.

Conventional inverse tone-mapping (ITM) methods tend to produce contrast distortions such as contrast loss and contrast reversal in reconstructed high dynamic range (HDR) images. This paper proposes a novel ITM optimization framework based on the assumption that the input low dynamic range (LDR) image is similar to the LDR image obtained by tone mapping a true HDR image. In the proposed framework, an HDR image is initially reconstructed by applying a conventional tone-mapping function in a reverse manner, and then the reconstructed HDR image is iteratively modified toward the optimum HDR image by minimizing the difference between the input LDR image and a tone-mapped LDR image obtained from the reconstructed HDR image. The experimental results demonstrate that the proposed framework effectively reconstructs a high-quality HDR image and outperforms other conventional methods in terms of objective quality.

High dynamic range imaging (HDRI) by bracketing of low dynamic range (LDR) images is demanding, as the sensor is deliberately operated at saturation. This exacerbates any crosstalk, interpixel capacitance, blooming and smear, all causing interpixel correlations (IC) and a deteriorated modulation transfer function (MTF). Established HDRI algorithms exclude saturated pixels, but generally overlook IC. This work presents a calibration method to estimate the affected region from saturated pixels for a color filter array (CFA) sensor, using the native CFA as a matched filter. The method minimizes color crosstalk given a set of candidates for proximity regions, and requires no special setup. Results are shown for a 21-bit HDR output image with improved color fidelity and reduced noise. The calibration reduces IC in the LDR images and is performed only once for a given sensor. The improvement is applicable to any HDRI algorithm based on CFA image bracketing, irrespective of sensor technology. Generalizations to subsaturated and supersaturated pixels are described, facilitating a suggested irradiance-exposure dependent point spread function charge repatriation strategy. Funding agencies: gonioLabs AB, Stockholm, Sweden

Widespread high dynamic range (HDR) video distribution via transmission and broadcast is imminent in the near future. However, the rate control (RC) algorithms in video coding standards, like high-efficiency video coding (HEVC), are optimized and designed for low dynamic range (LDR) content—making them inefficient in the rate-distortion (RD) sense, when applied to HDR video compression and distribution. In this paper, we propose a non-normative change to the HM16.2 HEVC $$\lambda $$ -domain RC algorithm to achieve high-fidelity HDR video distribution. A new $$\lambda $$ -QP relation is modeled for HDR content after assessing the suitable RD model. The results indicate that the proposed RC algorithm outperforms the default RC algorithm in HEVC, achieving on average performance gains of 1.40 dB, 0.685 units, and 0.0155 units in terms of PU-PSNR, HDR-VDP-2.2 Q factor, and HDR-VQM, respectively. Further, proposed method has bit estimation accuracy similar to the default HEVC RC algorithm. Also, the subjective evaluations corroborate the significance of the performance gained in the objective metrics.

In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet. This problem comprises two intractable challenges of how to handle saturation and noise properly and how to tackle misalignments caused by object motion or camera jittering. To address the former, we adopt a spatial attention module to adaptively select the most appropriate regions of various exposure low dynamic range (LDR) images for fusion. For the latter one, we propose to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable (PCD) alignment module. The proposed ADNet shows state-of-the-art performance compared with previous methods, achieving a PSNR-$l$ of 39.4471 and a PSNR-$\mu$ of 37.6359 in NTIRE 2021 Multi-Frame HDR Challenge., Comment: Accepted by CVPRW 2021

Imaging under low-light conditions is a challenging but important problem due to low dynamic range, image noise, and blurriness. In this work, we propose blur-free low-light imaging techniques by combining a conventional color camera with an event camera. The event camera complements the color camera by measuring brightness changes asynchronously at high speed with high dynamic range. We synchronize the two sensors with external trigger cable. We align the viewpoints of the event and color using a beamsplitter. We co-calibrate the two cameras geometrically. We derive an image formation model and use the inverted model to reduce the blurriness in color images. Experimental results demonstrate the effectiveness of our method.

Images taken in low-light environment often face with low dynamic range or color shift caused by lack of illumination. Although many algorithms have been proposed to enhance these images, not much effort has been made on quality assessment of these enhancement results. In our work, we built a database which contains 30 low-light images (both outdoor and indoor scene included) and enhanced images processed by 11 enhancement algorithms. We conducted a subjective experiment based on this database. We found that no algorithm can behave best with images in all situations. Generally, learning-based methods behave better. Further, we also did objective assessment on the database.

High dynamic range (HDR) technology enables the capture, storage, transmission and display of real-world lighting at a high precision as opposed to traditional low dynamic range (LDR) imaging. One of HDR’s main features is its ability to reproduce very bright and very dark areas simultaneously. Dynamic range describes the span between these extrema in the brightness scale. HDR research investigates the generation, capturing, processing, transmission, storage and reproduction of HDR content. Cultural heritage represents our legacy that must be passed on to future generations. As it is increasingly threatened with deterioration, destruction and disappearance, its documentation, conservation and presentation is of high importance. Given the real-world dynamic range and the limitations of conventional capture and display technology, HDR imaging represents an invaluable tool for accurate documentation, virtual reconstruction and visualisation of cultural heritage. HDR is used by academics, museums, and media to visualise the appearance of sites in various periods in time. Physically-based 3D virtual reconstructions are used for studying existing or ruined cultural heritage environments. This in turn enables archaeologists to interpret the past and deduce new historical knowledge. In this chapter we present the HDR pipeline, along with its use for cultural heritage preservation, recreation and presentation.

Tone-mapping-operators (TMOs) provide a useful means for converting high dynamic range (HDR) images to low dynamic range (LDR) images so that they can be viewed on standard displays, but this may influence the visual quality of experience (QoE) of the end-user. There is a need to understand the impact of TMOs to inform the choice of TMO algorithms for different displays, especially for small-screen-devices (SSDs) such as those used in mobile phones. This is important, as mobile devices are becoming the primary means of consuming multimedia contents. However, few studies have been undertaken to assess the impact of TMOs and viewing devices (especially SSDs) on the visual QoE of the user when using. In this chapter, we evaluate subjectively and objectively, the commonly used TMOs in different displays and resolutions for colour and grey-scale HDR images. Our results show that viewing devices have an influence on the TMOs performance, suggesting the need for a careful choice of TMO to enhance the viewing-QoE of the end-user. As expected, the higher resolution, the better HDR-image quality. Surprisingly, there was no significant difference between the Mean of Opinion Score (MOS) scores for colour and grey-scale images in SSDs. The device and TMOs affect QoE for colour and grey HDR-image equally. We found Shannon entropy (SE) to be a good objective measure of quality for colour and grey HDR images, suggesting that entropy may find use in automated HDR quality control assessment schemes, while; HDR-VDP-2 is a good objective measure for colour HDR image only.

Feedback inhibitory motifs are thought to be important for pattern separation across species. How feedback circuits may implement pattern separation of biologically plausible, temporally structured input in mammals is, however, poorly understood. We have quantitatively determined key properties of netfeedback inhibition in the mouse dentate gyrus, a region critically involved in pattern separation. Feedback inhibition is recruited steeply with a low dynamic range (0% to 4% of active GCs), and with a non-uniform spatial profile. Additionally, net feedback inhibition shows frequency-dependent facilitation, driven by strongly facilitating mossy fiber inputs. Computational analyses show a significant contribution of the feedback circuit to pattern separation of theta modulated inputs, even within individual theta cycles. Moreover, pattern separation was selectively boosted at gamma frequencies, in particular for highly similar inputs. This effect was highly robust, suggesting that frequency-dependent pattern separation is a key feature of the feedback inhibitory microcircuit., eLife digest You can probably recall where you left your car this morning without too much trouble. But assuming you use the same busy parking lot every day, can you remember which space you parked in yesterday? Or the day before that? Most people find this difficult not because they cannot remember what happened two or three days ago, but because it requires distinguishing between very similar memories. The car, the parking lot, and the time of day were the same on each occasion. So how do you remember where you parked this morning? This ability to distinguish between memories of similar events depends on a brain region called the hippocampus. A subregion of the hippocampus called the dentate gyrus generates different patterns of activity in response to events that are similar but distinct. This process is called pattern separation, and it helps ensure that you do not look for your car in yesterday’s parking space. Pattern separation in the dentate gyrus is thought to involve a form of negative feedback called feedback inhibition, a phenomenon where the output of a process acts to limit or stop the same process. To test this idea, Braganza et al. studied feedback inhibition in the dentate gyrus of mice, before building a computer model simulating the inhibition process and supplying the model with two types of realistic input. The first consisted of low-frequency theta brainwaves, which occur, for instance, in the dentate gyrus when animals explore their environment. The second consisted of higher frequency gamma brainwaves, which occur, for example, when animals experience something new. Testing the model showed that feedback inhibition contributes to pattern separation with both theta and gamma inputs. However, pattern separation is stronger with gamma input. This suggests that high frequency brainwaves in the hippocampus could help animals distinguish new events from old ones by promoting pattern separation. Various brain disorders, including Alzheimer’s disease, schizophrenia and epilepsy, involve changes in the dentate gyrus and altered brain rhythms. The current findings could help reveal how these changes contribute to memory impairments and to a reduced ability to distinguish similar experiences.

High-dynamic-range (HDR) photography involves fusing a bracket of images taken at different exposure settings in order to compensate for the low dynamic range of digital cameras such as the ones used in smartphones. In this paper, a method for automatically selecting the exposure settings of such images is introduced based on the camera characteristic function. In addition, a new fusion method is introduced based on an optimization formulation and weighted averaging. Both of these methods are implemented on a smartphone platform as an HDR app to demonstrate the practicality of the introduced methods. Comparison results with several existing methods are presented indicating the effectiveness as well as the computational efficiency of the introduced solution.

Tone mapping operators are designed to display high dynamic range (HDR) images on low dynamic range devices. Clustering-based content and color adaptive tone mapping algorithm aims to maintain the color information and local texture. However, fine details can still be lost in low dynamic range images. This paper presents an effective way of clustering-based content and color adaptive tone mapping algorithm by using fast search and find of density peak clustering. The suggested clustering method reduces the loss of local structure and allows better adaption of color in images. The experiments are carried out to evaluate the effectiveness and performance of proposed technique with state-of-the-art clustering techniques. The objective and subjective evaluation results reveal that fast search and find of density peak preserves more textural information. Therefore, it is most suitable to be used for clustering-based content and color adaptive tone mapping algorithm.

Unravelling the dynamic molecular interplay behind complex physiological processes such as neuronal plasticity requires the ability to both detect minute changes in biochemical states in response to physiological signals and track multiple signalling activities simultaneously. Fluorescent protein-based biosensors have enabled the real-time monitoring of dynamic signalling processes within the native context of living cells, yet most commonly used biosensors exhibit poor sensitivity (for example, due to low dynamic range) and are limited to imaging signalling activities in isolation. Here, we address this challenge by developing a suite of excitation ratiometric kinase activity biosensors that offer the highest reported dynamic range and enable the detection of subtle changes in signalling activity that could not be reliably detected previously, as well as a suite of single-fluorophore biosensors that enable the simultaneous tracking of as many as six distinct signalling activities in single living cells.

High dynamic range (HDR) images greatly improve visual content quality, but pose challenges in processing, acquisition, and display. Images captured in real-world scenarios with multiple nonlinear cameras, extremely short unknown exposure time, and a shared light source present the additional challenges of incremental baseline and angle deviation amongst the cameras. The disparity maps in such conditions are not reliable; therefore, we propose a method that relies on the accurate detection and matching of feature points across adjacent viewpoints. We determine the exposure gain among the matched feature points in the involved views and design an image restoration method to restore multiview low dynamic range (MVLDR) images for each viewpoint. Finally, the fusion of these restored MVLDR images produces high-quality images for each viewpoint without capturing a series of bracketed exposure. Extensive experiments are conducted in controlled and uncontrolled conditions, and results prove that the proposed method competes for the state-of-the-arts.

One way to optimize their operation of Concentrated Solar Power (CSP) plants is to obtain accurate short-term forecasts of Direct Normal Irradiance (DNI). To do so, the use of sky imagers is developing. They should provide high-quality images, especially in the circumsolar area, where pixels are usually saturated. To reduce the saturated area around the Sun, High Dynamic Range (HDR) imaging is needed. This paper describes the steps required to obtain HDR images from a sequence of low dynamic range images acquired using a sky imager. Since the HDR image will be used to study the sky radiance and the circumsolar area, a particular care is taken to maintain a linear sensor’s response throughout the process.

This paper presents a high dynamic range (HDR) imaging method that generates and fuses multiple exposures from a single image. We approximately estimate illumination using an edge-preserving smoothing filter and adjusts it to several levels for generating multiple illuminations that correspond to multiple exposures in HDR image acquisition techniques. The low dynamic range (LDR) images of multi-exposures are blended for reconstructing an HDR or tone-mapped-like LDR image for HDR/LDR displays.

In this work, we proposed a novel depth adaptive tone mapping scheme for stereo HDR imaging and 3D display. We are interested in the case where different exposures are taken from different viewpoints. The scheme employed a new depth-adaptive cross-trilateral filter (DA-CTF) for recovering High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images captured at different exposure levels. Explicitly leveraging additional depth information in the tone mapping operation correctly identify global contrast change and detail visibility change by preserving the edges and reducing halo artifacts in the synthesized 3D views by depth-image-based rendering (DIBR) procedure. The experiments show that the proposed DA-CTF and DIBR scheme outperforms state-of-the-art operators in the enhanced depiction of tone mapped HDR stereo images on LDR displays.

High Dynamic Range (HDR) images have attracted a lot of attention, since they could convey more information than Low Dynamic Range (LDR) images. The dynamic range of classic monitors is about 102:1, while the dynamic range of HDR images can reach 102:1. Therefore, HDR images cannot be fully displayed on traditional low dynamic range devices. To deal with this difficulty, many tone mapping algorithms have been proposed in the literature to compress the dynamic range of HDR images. This paper develops a new tone mapping algorithm based on multiscale morphological image processing. The proposed algorithm realizes effective dynamic range compression for various types of HDR images while avoiding the halo artifacts that usually exist in the results of multiscale tone mapping algorithms. Numerous experiments show that, compared with classical tone mapping operators, the proposed algorithm could perform better in preserving details, and the resulting images are more consistent with human visual perception.

Due to the higher exposure, the information in the highlight area usually clipped. This is one of the bottlenecks for the generation of high dynamic range (HDR) image. In this paper, we describe a simple technique to retrieve the information from an overexposed area by creating low dynamic range (LDR) image set from a single LDR image. These images are fused to generate HDR image. We evaluate our result by the single image HDR generation technique. Our technique shows better visual results compared to other techniques.

While inverse tone mapping (ITM) was frequently used for graphics rendering in the high dynamic range (HDR) space, the advent of HDR TVs and the consequent need for HDR multimedia contents open up new horizons for the consumption of ultra-high quality video contents. Unfortunately, previous methods are not appropriate for HDR TVs, and their inverse-tone-mapped results are not visually pleasing with noise amplification or lack of details. In this paper, we first present the ITM problem for HDR TVs and propose a CNN-based architecture, called ITM-CNN, which restores lost details and local contrast with its training strategy for enhancing the performance based on image decomposition using the guided filter. We demonstrate the benefits of decomposing the image by experimenting with various architectures and also compare the performance for different training strategies. Our ITM-CNN is a powerful means to solve the lack of HDR video contents with legacy LDR videos.

For indoor scenes, the fourth-order spherical harmonic function is used to model the illumination, resulting in that 48 spherical harmonic coefficients are used to represent the whole scene. The illumination contained in the low dynamic range image is insufficient, so high dynamic range environment maps are adopted in this part, and the aim is to predict spherical harmonic coefficients of the corresponding high dynamic range image from the low dynamic range image. For this problem, the MSE loss function is used in this paper. Experiments verify the effectiveness of our method. The final visual results show that our method can predict accurate spherical harmonic coefficients, and the recovered luminance is realistic.

High-dynamic-range (HDR) technology is an emerging video technology that allows displays to produce a higher range of luminance to better approximate the range of brightness perceived by the human eye. However, during the transition to this new technology, not all consumer devices will support the full range of luminance values offered by HDR. In order to also support these devices with lower dynamic ranges, content providers have to supply multiple dynamic range versions to provide the best experience to all viewers. This means that the processing cost to compress these versions will be multiplied by the number of versions. As a solution, this paper proposes a simultaneous encoder based on high efficiency video coding. This encoder reuses parts of the coding information generated during compression of an HDR video to accelerate the encoding of a low-dynamic-range (LDR) version of the same video. The proposed method speeds up the encoder 299 times with a bit rate increase of 12.4% compared to a non-accelerated encode of the LDR version. This is more than 90 times faster compared to state-of-the-art fast encoding algorithms and allows simultaneous encoding of the two versions for approximately the computational cost of a single encoder(1).

In this paper, we proposed a new infrared image detail enhancement approach. This approach could not only achieve the goal of enhancing the digital detail, but also make the processed image much closer to the real situation. Inspired by the joint-bilateral filter, two adjacent images were utilized to calculate the kernel functions in order to distinguish the detail information from the raw image. We also designed a new kernel function to modify the joint-bilateral filter and to eliminate the gradient reversal artifacts caused by the non-linear filtering. The new kernel is based on an adaptive emerge coefficient to realize the detail layer determination. The detail information was modified by the adaptive emerge coefficient along with two key parameters to realize the detail enhancement. Finally, we combined the processed detail layer with the base layer and rearrange the high dynamic image into monitor-suited low dynamic range to achieve better visual effect. Numerical calculation showed that this new technology has the best value compare to the previous research in detail enhancement. Figures and data flowcharts were demonstrated in the paper.

We study suitability of HDR and tone mapped imagery for detection of feature points.We test 6 detectors using 16 image formats under various scene changes.Results show that current FP detectors are not well tuned to process HDR images.Tone mapping is the best contemporary solution for FP detection in HDR imagery.A gradient-based local tone mapper with contrasts boosting produces best results. This paper evaluates the suitability of High Dynamic Range (HDR) imaging techniques for Feature Point (FP) detection under demanding lighting conditions. The FPs are evaluated in HDR, tone mapped HDR, and traditional Low Dynamic Range (LDR) images. Eleven global and local tone mapping operators are evaluated and six widely used FP detectors are used in the experiments (Harris, Shi-Tomasi, DoG, Fast Hessian, FAST, and BRISK). The distribution and repeatability rate of FPs are studied under changes of camera viewpoint, camera distance, and scene lighting. The results of the experiments show that current FP detectors cannot cope with HDR images well. The best contemporary solution is thus tone mapping of HDR images using a local tone mapper as a pre-processing step.

High dynamic range (HDR) imaging has gained momentum with the growth in digital photography technology. In this paper, performance assessment of various tone mapping techniques has been presented. HDR images were used and Tone Mapping Operators (TMOs) such as Gamma Correction, Reinhard, Drago, Ashkmin were applied to generate Low Dynamic Range (LDR) images. After that, LDR images are rated using objective assessment parameters such as luminance, mean, MSE and mPSNR. A new modified Ashikmin TMO is reported in this work. The results have shown that modified Ashikhmin has generated better LDR image than other TMOs. In addition, the profiles of objective performance assessment were also included to compare the usability of TMOs for generating better LDR images.

Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.

In this paper, we proposed a new infrared image detail enhancement approach. This approach could not only achieve the goal of enhancing the digital detail, but also make the processed image much closer to the real situation. Inspired by the joint-bilateral filter, two adjacent images were utilized to calculate the kernel functions in order to distinguish the detail information from the raw image. We also designed a new kernel function to modify the joint-bilateral filter and to eliminate the gradient reversal artifacts caused by the non-linear filtering. The new kernel is based on an adaptive emerge coefficient to realize the detail layer determination. The detail information was modified by the adaptive emerge coefficient along with two key parameters to realize the detail enhancement. Finally, we combined the processed detail layer with the base layer and rearrange the high dynamic image into monitor-suited low dynamic range to achieve better visual effect. Numerical calculation showed this new technology has the best value compare to the previous research in detail enhancement. Figures and data flowcharts were demonstrated in the paper.