Features Preserving Blurred Image Classification Using Large Language Model takes as input an image and a human-written instruction for how to improve that image. The neural model performs all-in-one image restoration. It achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement.
Abstract
Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model. In this work, we present the first approach that uses human-written instructions to guide the image restoration model. Given natural language prompts, our model can recover high-quality images from their degraded counterparts, considering multiple degradation types. Our method, Features Preserving Blurred Image Classification Using Large Language Model, achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement.Features Preserving Blurred Image Classification Using Large Language Model improves +1dB over previous all-in-one restoration methods. Moreover, our dataset and results represent a novel benchmark for new research on text-guided image restoration and enhancement.
Sometimes the blur, rain, or film grain noise are pleasant effects and part of the "aesthetics". Here we show a simple example on how to interact with InstructIR.
| Input | (1) I love this photo, could you remove the raindrops? please keep the content intact | (2) Can you make it look stunning? like a professional photo |
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| Input | (1) my image is too dark, I cannot see anything, can you fix it? | (2) Great it looks nice! can you apply tone mapping? |
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| Input | (1) can you remove the tiny dots in the image? it is very unpleasant | (2) now please inprove the quality and resolution of the picture |
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