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Shadow removal is a challenging problem due to its sensitivity to lighting and material conditions. In this paper, we propose a shading-aware shadow processing algorithm, which can automatically detect and remove complex shadows from a single color image. Our framework consists of two key steps. We firstly conduct a shadow-preserving filter upon the image which will effectively remove the image texture while preserving the shadow and shading information. Shadow regions are estimated by establishing a confidence map from the filtered image incorporating depth cue. We then develop a shading-aware optimization framework to remove shadows and recover shading in these regions. The extensive experimental results show that the proposed algorithm produces visually compelling results in a series of challenging images and it can handle complex shadows in both indoor and outdoor scenes. Quantitative and qualitative comparisons with current state-of-the-art methods strongly demonstrate the efficacy of our proposed approach. [ABSTRACT FROM AUTHOR]

Shadow removal is a fundamental and challenging problem in image processing field. Current approaches can only process shadows with simple scenes. For complex texture and illumination, the performance is less impressive. In this paper, we propose a novel shadow removal algorithm based on multi-scale image decomposition, which can recover the illumination for complex shadows with inconsistent illumination and different surface materials. Independent of shadow detection, our algorithm only requires a rough boundary distinguishing shadow regions from non-shadow regions. It first performs a multi-scale decomposition for the input image based on an illumination-sensitive smoothing process and then removes shadows in the basic layer using a local-to-global optimization strategy, which fuses all local shadow-free results in a global manner. Finally, we recover the texture details for the shadow-free basic layer and obtain the final shadow-free image. We validate the performance of the proposed method under various lighting and texture conditions and show consistent illumination between shadow and surrounding regions in the shadow removal results. [ABSTRACT FROM AUTHOR]

Handheld scanning using commodity depth cameras provides a flexible and low-cost manner to get 3D models. The existing methods scan a target by densely fusing all the captured depth images, yet most frames are redundant. The jittering frames inevitably embedded in handheld scanning process will cause feature blurring on the reconstructed model and even trigger the scan failure (i.e., camera tracking losing). To address these problems, in this paper, we propose a novel sparse-sequence fusion (SSF) algorithm for handheld scanning using commodity depth cameras. It first extracts related measurements for analyzing camera motion. Then based on these measurements, we progressively construct a supporting subset for the captured depth image sequence to decrease the data redundancy and the interference from jittering frames. Since SSF will reveal the intrinsic heavy noise of the original depth images, our method introduces a refinement process to eliminate the raw noise and recover geometric features for the depth images selected into the supporting subset. We finally obtain the fused result by integrating the refined depth images into the truncated signed distance field (TSDF) of the target. Multiple comparison experiments are conducted and the results verify the feasibility and validity of SSF for handheld scanning with a commodity depth camera. [ABSTRACT FROM AUTHOR]

Illumination decomposition for a single photograph is an important and challenging problem in image editing operation. In this paper, we present a novel coarse-to-fine strategy to perform illumination decomposition for photograph with multiple light sources. We first reconstruct the lighting environment of the image using the estimated geometry structure of the scene. With the position of lights, we detect the shadow regions as well as the highlights in the projected image for each light. Then, using the illumination cues from shadows, we estimate the coarse illumination decomposed image emitted by each light source. Finally, we present a light-aware illumination optimization model, which efficiently produces the finer illumination decomposition results, as well as recover the texture detail under the shadow. We validate our approach on a number of examples, and our method effectively decomposes the input image into multiple components corresponding to different light sources. [ABSTRACT FROM AUTHOR]