“Photography captures the light of a moment, but artificial intelligence gives us the power to rediscover the beauty hidden within it. Every shadow holds a secret, every faded image tells a story, and every restoration offers the past another opportunity to shine.” – MJ Martin
A Comprehensive Guide to Photo Restoration, Recovery, Enhancement, and Adjustment with ChatGPT
Introduction
Artificial intelligence is transforming photography, giving photographers powerful new methods for restoring damaged photographs, improving image quality, and manipulating light, colour, contrast, and composition. For Canadian photographers, archivists, historians, and enthusiasts, these capabilities offer opportunities to preserve our visual heritage while extending the creative possibilities of digital photography.
ChatGPT makes sophisticated image editing accessible through ordinary language. Instead of navigating dozens of adjustment panels, photographers can describe the result they want, from repairing a century-old family portrait to improving a modern digital photograph.
/goldenhour
Understanding ChatGPT Slash Commands
An important technical distinction must be established. ChatGPT does not have a published, built-in catalogue of photography-specific slash commands. OpenAI documents certain genuine slash commands in its desktop application, but commands such as /restore, /enhance, /sharpen, and /goldenhour are not documented as native photography commands.
However, these expressions can serve as convenient shorthand within a descriptive photographic prompt. They are user-defined labels, not guaranteed software instructions. The accompanying natural-language explanation determines what the AI is being asked to accomplish.
/goldenhour, /restore, /enhance
A Practical Photographic Vocabulary
The accompanying spreadsheet organizes 176 proposed shortcuts into twelve categories covering archival restoration, exposure, colour science, sharpening, lighting, optical correction, composition, portraits, monochrome, and delivery. Each entry explains its function, supplies a complete prompt, and assesses fidelity risk. These are reusable instructions rather than software functions.
For example, gamma concerns midtone brightness, dynamic range describes the separation between usable dark and bright detail, and colour gamut defines representable colours. Asking AI to improve all three resembles asking a darkroom technician for a better print. The objective is meaningful, but not proof of calibrated mathematical adjustment.
/restore, /background, /enhance
Preservation Versus Invention
Historical restoration presents a fundamental challenge. AI can suppress dust, repair apparent scratches, improve tonal separation, and reconstruct plausible textures. However, reconstructing an obscured face is like filling missing pages in a family diary. The result may appear convincing without being historically true.
Archival prompts should explicitly protect facial identity, inscriptions, clothing, framing, and authentic film grain. Colourization, newly created background detail, and changed lighting must be treated as interpretations, not recovered evidence.
/restore /enhance
A More Reliable Workflow
Upload the highest-quality original available, specify what should change, and identify what must remain unchanged. One useful instruction is: “Restore scratches, enrich black-and-white tonal depth, and improve existing eye detail. Preserve the person’s identity, original composition, and monochrome finish. Do not invent missing detail.”
Inspect the result, then request a single adjustment, such as softer shadows or less sharpening. OpenAI recommends specific constraints and incremental corrections because they help maintain consistency. Compare every version with the source before accepting it.
Conclusion and Questions
The opportunity is not a secret command vocabulary. It is the translation of photographic expertise into precise instructions. The workbook provides a searchable reference for Canadian family archives, professional studios, and conservation projects.
Two questions deserve consideration.
– Should restoration prioritize historical accuracy or emotional impact?
– When reconstruction becomes unavoidable, how clearly should the result be labelled?
These decisions determine whether AI photography represents responsible preservation or persuasive reinvention.
Excel Photography Command Reference
Here is a spreadsheet set of a dozen tabs including 176 photography prompts for you to download and use freely for your own image processing.
176 photography prompts • 12 workflows • 28 official desktop commands • Documentation and limitations
The workbook contains filterable commands, descriptions of their functions, complete copy-ready instructions, and guidance for protecting photographic authenticity.
About the Author:
Michael Martin is the Vice President of Technology with Metercor Inc., a Smart Meter, IoT, and Smart City systems integrator based in Canada. He has more than 40 years of experience in systems design for applications that use broadband networks, optical fibre, wireless, and digital communications technologies. He is a business and technology consultant. He was a senior executive consultant for 15 years with IBM, where he worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX).
Martin served on the Board of Directors for TeraGo Inc (TGO: TSX) and on the Board of Directors for Avante Logixx Inc. (XX: TSX.V). He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology. He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now Ontario Tech University] and on the Board of Advisers of five different Colleges in Ontario – Centennial College, Humber College, George Brown College, Durham College, Ryerson Polytechnic University [now Toronto Metropolitan University]. For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section.
He holds three master’s degrees – in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and seven major certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has completed over 80 next generation MOOC (Massive Open Online Courses) [aka Micro Learning] continuous education programs in a wide variety of topics, including: Economics, Python Programming, Internet of Things, Cloud, Artificial Intelligence and Cognitive systems, Blockchain, Agile, Power BI, Big Data, Design Thinking, Security, Indigenous Canada awareness, and more.
Martin is a volunteer, a photographer, a learner, a technologist, a pilot, a philosophizer, and always, a romantic optimist.