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Image filtering is a fundamental preprocessing step for accurate, robust computer vision applications such as image segmentation, object classification, and reconstruction. However, many convolutional neural network (CNN)‐based methods tend to lose significant edge information in the output layer, and generate undesired artefacts in the feature extraction layers. This study presents a deep CNN model for edge‐aware image filtering. The proposed network model consists of three sub‐networks: (i) feature extraction, (ii) convolution artefact removal, and (iii) structure extraction networks. The proposed network model has an end‐to‐end trainable architecture that does not need any post‐processing steps. Especially, the structure extraction network can successfully preserve significant edges. The proposed filter outperforms state‐of‐the‐art denoising filters in terms of both objective and subjective measures, and can be used for various image enhancement and restoration problems such as edge‐preserving smoothing, image denoising, deblurring, and deblocking. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient haze removal and image enhancement methods. The major contribution of the proposed research is two-fold: (i) an accurate atmospheric light estimation using principal component analysis, and (ii) learning-based transmission estimation. To reduce the computational cost, we impose a constraint on the candidate pixels to estimate the haze components in the sub-image. In addition, the proposed method extracts modified haze-relevant features to estimate an accurate transmission using random forest. Experimental results show that the proposed method can provide high-quality results with a significantly reduced computational load compared with existing methods. In addition, we demonstrate that the proposed method can significantly enhance the contrast of low-light images according to the assumption on the visual similarity between the inverted low-light and haze images. [ABSTRACT FROM AUTHOR]

This paper presents a low-light video restoration algorithm using similar patches among temporally adjacent frames. The proposed artifact-free low-light video restoration algorithm consists of three steps: 1) brightness enhancement using similar patches among temporally adjacent frames and adaptive accumulation; 2) improved color assignment to reduce color distortion; and 3) image fusion for saturation reduction using the guide map. The proposed brightness enhancement step guarantees not to produce any undesired artifacts because of searching the most similar patches among given set of temporally adjacent frames. The color assignment and fusion steps enable a fully automatic color preservation and average brightness control. Experimental results show that the proposed algorithm can better restore high-quality videos without undesired artifacts such as noise amplification, flicker, color distortion, and brightness saturation. As a result, the proposed algorithm can be implemented in a wide range of digital imaging applications such as video surveillance systems and advanced driver assistance systems. [ABSTRACT FROM AUTHOR]

This paper presents an optimization-based low-light image enhancement method using spatially adaptive ???????? -norm based Retinex model. The proposed method adaptively enforces the regularization parameter using the spatially adaptive weight map, which is generated using the bright channel prior (BCP) and local variance map. Since the proposed weight map assigns the smaller weight value at the bright and edge region, the proposed method can perform weak noise reduction to preserve the edges and textures. In addition, the simplified version of the proposed method is presented using the FFT and quantized weight values for the application to consumer devices. Experimental results show that the proposed method can provide better enhanced result without the ι2 -norm minimization artifacts at the low computational cost. [ABSTRACT FROM PUBLISHER]

This paper presents a multi-frame superresolution approach to reconstruct a high-resolution image from several low-resolution video frames. The proposed algorithm consists of three steps: i) definition of a local search region for the optimal patch using motion vectors, ii) adaptive selection of the optimum patch based on lowresolution image degradation model, and iii) combination of the optimum patch and reconstructed image. As a result, the proposed algorithm can remove interpolation artifacts using directionally adaptive patch selection based on the lowresolution image degradation model. Moreover, superresolved images without distortion between consecutive frames can be generated. The proposed method provides a significantly improved super-resolution performance over existing methods in the sense of both subjective and objective measures including peak-to-peak signal-to-noise ratio (PSNR), structural similarity measure (SSIM), and naturalness image quality evaluator (NIQE). The proposed multi-frame super-resolution algorithm is designed for realtime video processing hardware by reducing the search region for optimal patches, and suitable for consumer imaging devices including ultra-high-definition (UHD) digital televisions, surveillance systems, and medical imaging systems for image restoration and enhancement. [ABSTRACT FROM AUTHOR]

In this paper, a novel image enhancement system for a wide-angle lens camera is presented. The proposed system consists of; i) lens distortion correction using space-varying interpolation kernels and ii) image restoration based on the local self-similarity. The correction process for the geometric distortion produced by a wide-angle lens results in radial distortion artifacts caused by non-linear resampling. To reduce such artifacts, the proposed algorithm uses space-varying interpolation kernels derived from the lens calibration data. The corrected image is further enhanced using self-example-based image restoration. Experimental results demonstrate the proposed method can correctly remove the geometric distortion and further enhance the quality of the radially interpolated image. [ABSTRACT FROM AUTHOR]

In this paper, a novel dynamic range extension method is presented by detecting under-exposed regions and performing spatially adaptive contrast enhancement. More specifically, a combined color-channels transmission (CCT) map is proposed to detect low-contrast regions, and spatially adaptive gain computation is used to enhance the contrast of the under-exposed regions. As a result, the proposed method can extend the dynamic range of a single input image by changing the brightness of under-exposed regions with preserving the brightness of background. Moreover, the proposed method requires less computational load than existing multiple frames-based high dynamic range (HDR) imaging methods without color distortion. For these reasons, the proposed method can be applied to various imaging devices, such as mobile phone cameras, compact digital cameras, and video surveillance systems. [ABSTRACT FROM AUTHOR]