5 citations · 24 across the 18 of their papers we have counts for
21 papers
OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting
Atakan Topaloglu, Kunyi Li, Michael Niemeyer +3
Sparse-view novel view synthesis is fundamentally ill-posed due to severe geometric ambiguity. Current methods are caught in a trade-off: regressive models are geometrically faithf…
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution
M. Akin Yilmaz, Ahmet Bilican, A. Murat Tekalp
Balancing reconstruction quality versus model efficiency remains a critical challenge in lightweight single image super-resolution (SISR). Despite the prevalence of attention mecha…
Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets for Vision
Ahmet Bilican, M. Akın Yılmaz, A. Murat Tekalp +1
Efficiently adapting large pretrained models is critical under tight compute and memory budgets. While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA achieve efficiency t…
FG-DFPN: Flow Guided Deformable Frame Prediction Network
M. Akın Yılmaz, Ahmet Bilican, A. Murat Tekalp
Video frame prediction remains a fundamental challenge in computer vision with direct implications for autonomous systems, video compression, and media synthesis. We present FG-DFP…
A New Multi-Picture Architecture for Learned Video Deinterlacing and Demosaicing with Parallel Deformable Convolution and Self-Attention Blocks
Ronglei Ji, A. Murat Tekalp
Despite the fact real-world video deinterlacing and demosaicing are well-suited to supervised learning from synthetically degraded data because the degradation models are known and…
PAON: A New Neuron Model using Padé Approximants
Onur Keleş, A. Murat Tekalp
Convolutional neural networks (CNN) are built upon the classical McCulloch-Pitts neuron model, which is essentially a linear model, where the nonlinearity is provided by a separate…
Saliency-aware End-to-end Learned Variable-Bitrate 360-degree Image Compression
Oguzhan Gungordu, A. Murat Tekalp
Effective compression of 360 images, also referred to as omnidirectional images (ODIs), is of high interest for various virtual reality (VR) and related applications. 2D im…
Motion-Adaptive Inference for Flexible Learned B-Frame Compression
M. Akin Yilmaz, O. Ugur Ulas, Ahmet Bilican +1
While the performance of recent learned intra and sequential video compression models exceed that of respective traditional codecs, the performance of learned B-frame compression m…
Spatio-Temporal Perception-Distortion Trade-off in Learned Video SR
Nasrin Rahimi, A. Murat Tekalp
Perception-distortion trade-off is well-understood for single-image super-resolution. However, its extension to video super-resolution (VSR) is not straightforward, since popular p…
Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention
Ronglei Ji, A. Murat Tekalp
Although deep learning has made significant impact on image/video restoration and super-resolution, learned deinterlacing has so far received less attention in academia or industry…
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors
Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan
Assuming a known degradation model, the performance of a learned image super-resolution (SR) model depends on how well the variety of image characteristics within the training set…
Perception-Distortion Trade-off in the SR Space Spanned by Flow Models
Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan +2
Flow-based generative super-resolution (SR) models learn to produce a diverse set of feasible SR solutions, called the SR space. Diversity of SR solutions increases with the temper…
Two-stage domain adapted training for better generalization in real-world image restoration and super-resolution
Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan
It is well-known that in inverse problems, end-to-end trained networks overfit the degradation model seen in the training set, i.e., they do not generalize to other types of degrad…
Self-Organized Residual Blocks for Image Super-Resolution
Onur Keleş, A. Murat Tekalp, Junaid Malik +1
It has become a standard practice to use the convolutional networks (ConvNet) with RELU non-linearity in image restoration and super-resolution (SR). Although the universal approxi…
Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression
M. Akın Yılmaz, Onur Keleş, Hilal Güven +3
In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform…
On the Computation of PSNR for a Set of Images or Video
Onur Keleş, M. Akın Yılmaz, A. Murat Tekalp +2
When comparing learned image/video restoration and compression methods, it is common to report peak-signal to noise ratio (PSNR) results. However, there does not exist a generally…
A Practical Approach for Rate-Distortion-Perception Analysis in Learned Image Compression
Ogun Kirmemis, A. Murat Tekalp
Rate-distortion optimization (RDO) of codecs, where distortion is quantified by the mean-square error, has been a standard practice in image/video compression over the years. RDO s…
Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression
A. Murat Tekalp, Michele Covell, Radu Timofte +1
Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods. Hence, the stat…
Can Learned Frame-Prediction Compete with Block-Motion Compensation for Video Coding?
Serkan Sulun, A. Murat Tekalp
Given recent advances in learned video prediction, we investigate whether a simple video codec using a pre-trained deep model for next frame prediction based on previously encoded/…
Realizing a Low-Power Head-Mounted Phase-Only Holographic Display by Light-Weight Compression
Burak Soner, Erdem Ulusoy, A. Murat Tekalp +1
Head-mounted holographic displays (HMHD) are projected to be the first commercial realization of holographic video display systems. HMHDs use liquid crystal on silicon (LCoS) spati…
Learned Compression Artifact Removal by Deep Residual Networks
Ogün Kırmemiş, Gonca Bakar, A. Murat Tekalp
We propose a method for learned compression artifact removal by post-processing of BPG compressed images. We trained three networks of different sizes. We encoded input images usin…