Super resolution (SR) enables to generate a high-resolution (HR) image from one or more low-resolution (LR) images. Since a variety of CNN models have been recently studied in the areas of computer vision, these approaches have been combined with SR in order to provide higher image restoration. In this paper, we propose a lightweight CNN-based SR method, named multi-scale channel dense network (MCDN). In order to design the proposed network, we extracted the training images from the DIVerse 2K (DIV2K) dataset and investigated the trade-off between the SR accuracy and the network complexity. The experimental results show that the proposed method can significantly reduce the network complexity, such as the number of network parameters and total memory capacity, while maintaining slightly better or similar perceptual quality compared to the previous methods.

With the advancement of sensors, image and video processing have developed for use in the visual sensing area. Among them, video super-resolution (VSR) aims to reconstruct high-resolution sequences from low-resolution sequences. To use consecutive contexts within a low-resolution sequence, VSR learns the spatial and temporal characteristics of multiple frames of the low-resolution sequence. As one of the convolutional neural network-based VSR methods, we propose a deformable convolution-based alignment network (DCAN) to generate scaled high-resolution sequences with quadruple the size of the low-resolution sequences. The proposed method consists of a feature extraction block, two different alignment blocks that use deformable convolution, and an up-sampling block. Experimental results show that the proposed DCAN achieved better performances in both the peak signal-to-noise ratio and structural similarity index measure than the compared methods. The proposed DCAN significantly reduces the network complexities, such as the number of network parameters, the total memory, and the inference speed, compared with the latest method.

Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods.

Video super-resolution (VSR) aims to reconstruct high-resolution (HR) videos from low-resolution (LR) inputs by utilizing spatio-temporal correlations across consecutive LR frames. While recent advances in deep learning, particularly transformer-based architecture, have substantially improved VSR performance, maintaining spatio-temporal coherence remains a critical challenge. To address this issue, we propose a novel U-Net based spatial transformer module (USTM) that can be seamlessly integrated into the reconstruction stage of existing VSR frameworks. The proposed USTM combines a spatial transformer with a U-Net structure to extract fine-grained spatio-temporal features to enhance the reconstruction of complex motions and texture patterns. Extensive ablation studies were conducted to identify the optimal configuration and verify the contribution of each USTM component. For performance evaluations, USTM was incorporated into multiple representative VSR methods. Experimental results demonstrate that integrating USTM consistently enhances PSNR and SSIM scores on benchmark datasets, including REDS, Vimeo-90K, and Vid4. Furthermore, visual comparisons highlight the superiority of the proposed method, particularly in high-frequency regions, compared to baseline VSR methods without USTM.

Multi-scale rendering exposes scale-dependent reconstruction errors in 3D Gaussian splatting (3DGS) when the rendering resolution differs from the training resolution. Existing anti-aliasing and multi-scale optimization methods reduce these artifacts, but each Gaussian usually keeps a fixed color representation and a fixed spatial allocation after densification. This fixed representation is restrictive at coarse scales because one rendered pixel combines multiple Gaussian contributions into an average color that can differ from the color assigned to each Gaussian. In addition, Gaussians allocated by fine-scale reconstruction criteria may provide insufficient coverage for regions with persistent coarse-scale errors. This paper proposes adaptive-scale color offset and error-guided Gaussian reallocation for multi-scale 3DGS. The adaptive-scale color offset applies a scale-specific correction to the shared base color of each Gaussian while keeping its geometry unchanged. Error-guided Gaussian reallocation shifts low-contribution Gaussians toward high-error regions under a fixed primitive budget and preserves Gaussians that support coarse-scale rendering. Experimental results show that the proposed method improves multi-scale rendering quality across rendering scales on Mip-NeRF 360, Tanks and Temples, and Deep Blending, with larger gains at coarse scales. These results demonstrate that adapting Gaussian color and primitive allocation together improves scale-consistent 3DGS rendering without modifying the inference-stage rendering process.