FreqINR: Frequency Consistency for Implicit Neural Representation with Adaptive DCT Frequency Loss
Recent advancements in local Implicit Neural Representation (INR) demonstrate its exceptional capability in handling images at various resolutions. However, frequency discrepancies between high-resolution (HR) and ground-truth images, especially at larger scales, result in significant artifacts and blurring in HR images. This paper introduces Frequency Consistency for Implicit Neural Representation (FreqINR), an innovative Arbitrary-scale Super-resolution method aimed at enhancing detailed textures by ensuring spectral consistency throughout both training and inference. During training, we employ Adaptive Discrete Cosine Transform Frequency Loss (ADFL) to minimize the frequency gap between HR and ground-truth images, utilizing 2-Dimensional DCT bases and focusing dynamically on challenging frequencies. During inference, we extend the receptive field to preserve spectral coherence between low-resolution (LR) and ground-truth images, which is crucial for the model to generate high-frequency details from LR counterparts. Experimental results show that FreqINR, as a lightweight approach, achieves state-of-the-art performance compared to existing Arbitrary-scale Super-resolution methods and offers notable improvements in computational efficiency. The code for our method will be made publicly available.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencySuper-ResolutionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Frequency-Decomposed INR for NIR-Assisted Low-Light RGB Image Denoising
Addressing the issues of severe noise and high frequency structural degradation in visible images under low-light conditions, this paper proposes a Near Infrared (NIR) aided low light image restoration method based on Fr…
Image RestorationImage DenoisingFi-Gaussian: Frequency-Aware Implicit Gaussian Splatting for Single Image Dehazing
Single image dehazing continues to be hindered by the loss of high-frequency details and the difficulty of accurate physical scattering modeling. To address these issues, we propose Fi-Gaussian, a frequency-aware implici…
Single Image DehazingPoint CloudsFrequency Decoupled Framework for Screen Content Image Super-Resolution
Methods based on implicit neural representations have demonstrated superior performance in Screen Content Image Super-Resolution (SCISR) . However, they overlooked the inherent frequency characteristics, leading to subop…
Image Super-ResolutionFLAIR: Frequency- and Locality-Aware Implicit Neural Representations
Implicit Neural Representations (INRs) leverage neural networks to map coordinates to corresponding signals, enabling continuous and compact representations. This paradigm has driven significant advances in various visio…
3D Shape ReconstructionNovel View SynthesisAdaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representations
Fourier-encoded implicit neural representations (INRs) have shown strong capability in modeling continuous signals from discrete samples. However, conventional Fourier feature mappings use a fixed set of frequencies over…