This paper presents a novel and efficient image enhancement method based on pigment representation. Unlike conventional methods where the color transformation is restricted to pre-defined color spaces like RGB, our method dynamically adapts to input content by transforming RGB colors into a high-dimensional feature space referred to aspigments. The proposed pigment representation offers adaptability and expressiveness, achieving superior image enhancement performance. The proposed method involves transforming input RGB colors into high-dimensional pigments, which are then reprojected individually and blended to refine and aggregate the information of the colors in pigment spaces. Those pigments are then transformed back into RGB colors to generate an enhanced output image. The transformation and reprojection parameters are derived from the visual encoder which adaptively estimates such parameters based on the content in the input image. Extensive experimental results demonstrate the superior performance of the proposed method over state-of-the-art methods in image enhancement tasks, including image retouching and tone mapping, while maintaining relatively low computational complexity and small model size.

Federated learning (FL) often suffers from performance degradation due to key challenges such as data heterogeneity and communication constraints. To address these limitations, we present a novel FL framework called FedWSQ, which integrates weight standardization (WS) and the proposed distribution-aware non-uniform quantization (DANUQ). WS enhances FL performance by filtering out biased components in local updates during training, thereby improving the robustness of the model against data heterogeneity and unstable client participation. In addition, DANUQ minimizes quantization errors by leveraging the statistical properties of local model updates. As a result, FedWSQ significantly reduces communication overhead while maintaining superior model accuracy. Extensive experiments on FL benchmark datasets demonstrate that FedWSQ consistently outperforms existing FL methods across various challenging FL settings, including extreme data heterogeneity and ultra-low-bit communication scenarios.

In this letter, we propose an adaptive video demoiréing network (AVDNet), which dynamically suppresses moiré patterns in video environments by leveraging both the spectral and temporal characteristics of moiré artifacts. It consists of two key modules: the adaptive bandpass block (ABB) and the subtraction-guided alignment block (SGAB). ABB performs frame-wise demoiréing in the implicit frequency domain using an adaptive bandpass filter that modulates its response to match the moiré spectral characteristics of each frame. SGAB exploits subtraction maps between adjacent frames to guide alignment and suppress the temporal propagation of moiré artifacts. Experimental results demonstrate that AVDNet outperforms state-of-the-art methods quantitatively and qualitatively while maintaining a compact model size and low computational cost.

In this letter, we propose a multiscale coarse-to-fine guided screenshot demoireing algorithm. We first extract the multiscale features of the input image. Then, we develop the multiscale guided restoration block (MGRB), which removes moire patterns with the guidance of multiscale information by exploiting the correlation between moire frequencies. To this end, we design two blocks for feature modulation and moire pattern removal. In addition, to further improve the performance, we develop an adaptive reconstruction loss to direct the network to focus on regions that are difficult to restore. Experimental results on multiple datasets demonstrate that the proposed algorithm provides comparable or even better demoireing performance than state-of-the-art algorithms.

A novel contour-constrained superpixel (CCS) algorithm is proposed in this work. We initialize superpixels and regions in a regular grid and then refine the superpixel label of each region hierarchically from block to pixel levels. To make superpixel boundaries compatible with object contours, we propose the notion of contour pattern matching and formulate an objective function including the contour constraint. Furthermore, we extend the CCS algorithm to generate temporal superpixels for video processing. We initialize superpixel labels in each frame by transferring those in the previous frame and refine the labels to make superpixels temporally consistent as well as compatible with object contours. Experimental results demonstrate that the proposed algorithm provides better performance than the state-of-the-art superpixel methods.

A temporal superpixel algorithm based on proximity-weighted patch matching (TS-PPM) is proposed in this work. We develop the proximity-weighted patch matching (PPM), which estimates the motion vector of a superpixel robustly, by considering the patch matching distances of neighboring superpixels as well as the target superpixel. In each frame, we initialize superpixels by transferring the superpixel labels of the previous frame using PPM motion vectors. Then, we update the superpixel labels of boundary pixels, based on a cost function, composed of color, spatial, contour, and temporal consistency terms. Finally, we execute superpixel splitting, merging, and relabeling to regularize superpixel sizes and reduce incorrect labels. Experiments show that the proposed algorithm outperforms the state-of-the-art conventional algorithms significantly.

A novel RGB-D image segmentation algorithm is proposed in this work. This is the first attempt to achieve image segmentation based on the theory of multiple random walkers (MRW). We construct a multi-layer graph, whose nodes are superpixels divided with various parameters. Also, we set an edge weight to be proportional to the similarity of color and depth features between two adjacent nodes. Then, we segment an input RGB-D image by employing MRW simulation. Specifically, we decide the initial probability distribution of agents so that they are far from each other. We then execute the MRW process with the repulsive restarting rule, which makes the agents repel one another and occupy their own exclusive regions. Experimental results show that the proposed MRW image segmentation algorithm provides competitive segmentation performances, as compared with the conventional state-of-the-art algorithms.

A video saliency detection algorithm based on feature learning, called ROCT, is proposed in this work. To detect salient regions, we design multiple spatiotemporal features and combine those features using a support vector machine (SVM). We extract the spatial features of rarity, compactness, and center prior by analyzing the color distribution in each image frame. Also, we obtain the temporal features of motion intensity and motion contrast to identify visually important motions. We train an SVM classifier using the spatiotemporal features extracted from training video sequences. Finally, we compute the visual saliency of each patch in an input sequence using the trained classifier. Experimental results demonstrate that the proposed algorithm provides more accurate and reliable results of saliency detection than conventional algorithms.