Exploration of Lightweight Single Image Denoising with Transformers and Truly Fair Training
As multimedia content often contains noise from intrinsic defects of digital devices, image denoising is an important step for high-level vision recognition tasks. Although several studies have developed the denoising field employing advanced Transformers, these networks are too momory-intensive for real-world applications. Additionally, there is a lack of research on lightweight denosing (LWDN) with Transformers. To handle this, this work provides seven comparative baseline Transformers for LWDN, serving as a foundation for future research. We also demonstrate the parts of randomly cropped patches significantly affect the denoising performances during training. While previous studies have overlooked this aspect, we aim to train our baseline Transformers in a truly fair manner. Furthermore, we conduct empirical analyses of various components to determine the key considerations for constructing LWDN Transformers. Codes are available at https://github.com/rami0205/LWDN.
Code (2)
Tasks
DenoisingImage DenoisingSimilar Papers 제목 키워드 기반
MMDiff: Extending Diffusion Transformers for Multi-Modal Generation
Diffusion transformers have demonstrated remarkable generative capabilities, yet the rich perceptual representations computed across their denoising trajectory are discarded once the content is rendered. We present MMDif…
Synthetic Data GenerationSalient Object DetectionSemantic SegmentationDepth EstimationImage Recognition with Online Lightweight Vision Transformer: A Survey
The Transformer architecture has achieved significant success in natural language processing, motivating its adaptation to computer vision tasks. Unlike convolutional neural networks, vision transformers inherently captu…
Knowledge DistillationSurveyCalibri: Enhancing Diffusion Transformers via Parameter-Efficient Calibration
In this paper, we uncover the hidden potential of Diffusion Transformers (DiTs) to significantly enhance generative tasks. Through an in-depth analysis of the denoising process, we demonstrate that introducing a single l…
Image GenerationJoint Denoising of Cryo-EM Projection Images using Polar Transformers
Deep neural networks (DNNs) have proven powerful for denoising, but they are ultimately of limited use in high-noise settings, such as for cryogenic electron microscopy (cryo-EM) projection images. In this setting, howev…
Cryogenic Electron Microscopy (cryo-EM)DenoisingLiteVSR: Lightweight Adaptation of Frozen Diffusion Transformers for Video Super-Resolution
Adapting large-scale pre-trained video generators for Video Super-Resolution (VSR) in novel domains remains computationally prohibitive. Methods that reformulate generation as direct Low-Quality to High-Quality mappings …
Video Super-Resolution