49 research outputs found
ESTIMATION OF RELIABILITY OF SYSTEM OF REVERSE STAMPS WITH USE OF COMPOSITE MATERIALS AND ACCURACY OF PUNCHED PARTS
Estimation of reliability of system of reverse stamps with use of
composite materials and accuracy of punched parts
Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution
This paper presents a novel Diffusion-Wavelet (DiWa) approach for
Single-Image Super-Resolution (SISR). It leverages the strengths of Denoising
Diffusion Probabilistic Models (DDPMs) and Discrete Wavelet Transformation
(DWT). By enabling DDPMs to operate in the DWT domain, our DDPM models
effectively hallucinate high-frequency information for super-resolved images on
the wavelet spectrum, resulting in high-quality and detailed reconstructions in
image space. Quantitatively, we outperform state-of-the-art diffusion-based
SISR methods, namely SR3 and SRDiff, regarding PSNR, SSIM, and LPIPS on both
face (8x scaling) and general (4x scaling) SR benchmarks. Meanwhile, using DWT
enabled us to use fewer parameters than the compared models: 92M parameters
instead of 550M compared to SR3 and 9.3M instead of 12M compared to SRDiff.
Additionally, our method outperforms other state-of-the-art generative methods
on classical general SR datasets while saving inference time. Finally, our work
highlights its potential for various applications
Are Visual Recognition Models Robust to Image Compression?
Reducing the data footprint of visual content via image compression is
essential to reduce storage requirements, but also to reduce the bandwidth and
latency requirements for transmission. In particular, the use of compressed
images allows for faster transfer of data, and faster response times for visual
recognition in edge devices that rely on cloud-based services. In this paper,
we first analyze the impact of image compression using traditional codecs, as
well as recent state-of-the-art neural compression approaches, on three visual
recognition tasks: image classification, object detection, and semantic
segmentation. We consider a wide range of compression levels, ranging from 0.1
to 2 bits-per-pixel (bpp). We find that for all three tasks, the recognition
ability is significantly impacted when using strong compression. For example,
for segmentation mIoU is reduced from 44.5 to 30.5 mIoU when compressing to 0.1
bpp using the best compression model we evaluated. Second, we test to what
extent this performance drop can be ascribed to a loss of relevant information
in the compressed image, or to a lack of generalization of visual recognition
models to images with compression artefacts. We find that to a large extent the
performance loss is due to the latter: by finetuning the recognition models on
compressed training images, most of the performance loss is recovered. For
example, bringing segmentation accuracy back up to 42 mIoU, i.e. recovering 82%
of the original drop in accuracy
YODA: You Only Diffuse Areas. An Area-Masked Diffusion Approach For Image Super-Resolution
This work introduces "You Only Diffuse Areas" (YODA), a novel method for
partial diffusion in Single-Image Super-Resolution (SISR). The core idea is to
utilize diffusion selectively on spatial regions based on attention maps
derived from the low-resolution image and the current time step in the
diffusion process. This time-dependent targeting enables a more effective
conversion to high-resolution outputs by focusing on areas that benefit the
most from the iterative refinement process, i.e., detail-rich objects. We
empirically validate YODA by extending leading diffusion-based SISR methods SR3
and SRDiff. Our experiments demonstrate new state-of-the-art performance gains
in face and general SR across PSNR, SSIM, and LPIPS metrics. A notable finding
is YODA's stabilization effect on training by reducing color shifts, especially
when induced by small batch sizes, potentially contributing to
resource-constrained scenarios. The proposed spatial and temporal adaptive
diffusion mechanism opens promising research directions, including developing
enhanced attention map extraction techniques and optimizing inference latency
based on sparser diffusion.Comment: Brian B. Moser and Stanislav Frolov contributed equall
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances
With the advent of Deep Learning (DL), Super-Resolution (SR) has also become
a thriving research area. However, despite promising results, the field still
faces challenges that require further research e.g., allowing flexible
upsampling, more effective loss functions, and better evaluation metrics. We
review the domain of SR in light of recent advances, and examine
state-of-the-art models such as diffusion (DDPM) and transformer-based SR
models. We present a critical discussion on contemporary strategies used in SR,
and identify promising yet unexplored research directions. We complement
previous surveys by incorporating the latest developments in the field such as
uncertainty-driven losses, wavelet networks, neural architecture search, novel
normalization methods, and the latests evaluation techniques. We also include
several visualizations for the models and methods throughout each chapter in
order to facilitate a global understanding of the trends in the field. This
review is ultimately aimed at helping researchers to push the boundaries of DL
applied to SR.Comment: accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence, 202